Machine Learning Tools are Available if you Know Where to Look

Hello,

This is Simon with the latest edition of The Weekly. In these updates, I share key AI related stories from this week's news, list upcoming events, and share any longer form articles posted on the website.

Last week, I discussed the fact that generative AI tools like LLMs are not the best for data analysis, despite the ease with which it’s possible to ask a chatbot to analyse your data. This isn’t helped by the confident responses we have now come accustomed to. Remember that LLMs are just a next word predictor and are much better suited for creative writing and not number crunching. Instead of generative AI, we should use more traditional forms of AI, like machine learning, which are far more accurate for working with structured, organised tables of data, like those in a spreadsheet.  Despite this being the better approach, it’s not as easy to understand where you can access machine learning tools. Considering most of us are not trained data scientists, we need access to user-friendly tools. Below, I’ll introduce a few ways you can get access to these tools and run data analysis without being a programmer.

Both Google Sheets and Microsoft Excel offer excellent plugins that provide access to machine learning tools, such as Simple ML for Sheets and Analyze Data, respectively. These are the quickest and easiest ways for non-technical users to access machine learning. I would strongly advise you to take a look at these first if you want to dip your toes in the water.

There are also a whole host of software applications in the market that package up machine learning capabilities, including my last company, Dataiku, and more specialist solutions like Akkio. These platforms offer very powerful solutions to enterprise organisations and do not come cheaply. Unless your company is paying for these tools, it’s unlikely you will get access to them.

This is the one option that I strongly caution using. All the major platforms like Google, Microsoft and Amazon provide a whole suite of data and AI Tools. Amazon, for example, has AWS Sagemaker, which is a set of capabilities that allows you to run machine learning. Google has their own version called Vertex AI. With your own data set, you can train your own AI models very easily, and whilst this seems a tempting option, you can incur high costs extremely quickly. In full transparency, I thought I was being clever, training a model that took less than 10 minutes to create, only to find a bill for over £500 waiting for me. So take that as a warning.

Using traditional machine learning tools is entirely possible, and with a range of options, even if it is more involved than just asking ChatGPT. But if you want to start using AI in your data analysis for work, you need to make sure you are using the most accurate methods available to you.

Depending on whether you use Google Sheets or MS Excel, go ahead and install one of those plug-ins and see what AI work you can do with some data.

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In this section, I highlight a real-world example of AI. This week I talk about how AI can improve safety on building sites.

Governments that spent the last two years drafting AI guidelines are now quietly moving into the less glamorous, but far more consequential, phase: implementation. The EU’s AI Act, for example, is rolling out its first compliance requirements, forcing companies to document where their AI models come from and how they’re tested. Meanwhile, the US has been issuing sector-specific rules rather than a single omnibus law, especially in healthcare and hiring.

Why this matters: Regulation will directly influence how fast companies adopt AI, how trustworthy deployed systems become, and how protected employees are from poorly governed tools.

Microsoft, Google, Slack and smaller platforms continue embedding generative AI features into the tools people use every day — almost always enabled by default. The result is that employees who never sought out AI tools are still bumping into them across calendars, documents, email, and meeting notes. Employers meanwhile are wrestling with whether these assistants are genuinely improving productivity or simply expanding digital noise.

Why this matters: The AI transition isn’t happening because workers choose it. It’s happening because the software ecosystem is dragging them along.

Across 2023–24, customer support was the clearest early winner for generative AI, and that trend has only grown stronger. Companies are now using AI not just for chatbot triage but also to summarise calls, recommend responses, and handle routine tickets end-to-end. In many firms, human agents have shifted to handling only the hardest or most emotionally sensitive queries.

Why this matters: Customer support is a real-world example of how AI changes job design: fewer repetitive tasks, but more pressure on workers to solve complex problems quickly.

Thanks for reading, and see you next Friday.

Simon,

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